Temporal Motif-Enhanced Contrastive Learning for Adaptive Anomaly Detection in Dynamic Networks

Keywords: Anomaly Detection, Dynamic Networks, Temporal Graphs, Graph Neural Networks, Contrastive Learning, Temporal Motifs, Adaptive Learning, Self-Supervised Learning

TL;DR: We use temporal motifs (micro-patterns) to enhance a contrastive learning framework, enabling adaptive and accurate anomaly detection in dynamic networks without requiring extensive labeled data.

Abstract: We propose a novel framework for anomaly detection in dynamic networks that combines temporal motif analysis with contrastive graph neural networks. Our approach extracts temporal motifs as micro-dynamic patterns, processes them through a multi-scale GNN architecture, and uses adaptive contrastive learning to continuously update representations of normal behavior. This enables detection of both known and novel anomaly types without requiring extensive labeled data or frequent retraining. Experiments on four dynamic network datasets (CollegeMsg, Email-Eu-core, Higgs Twitter, Epinions) demonstrate 15–30\% improvement in F1-score over state-of-the-art methods across various anomaly types including communication anomalies, organizational deviations, information cascades, and iconic anomalies. The framework provides a foundation for adaptive monitoring systems that can operate in evolving network environments with minimal human intervention.

Submission Number: 307